Optimization Technique for Deep Learning Methodology on Power Side Channel Attacks

The first non-profiled side-channel attack (SCA) method using deep learning is Timon's Differential Deep Learning Analysis (DDLA). This method is effective in retrieving the secret key with the help of deep learning metrics. The Neural Network (NN) has to be trained numerous times since the proposed approach increases the learning cost with the key sizes, making it hard to assess the results from the intermediate stage. In this research, we provide three possible answers to the issues raised above, along with any challenges that could result from trying to solve these issues. We will start by offering an updated algorithm that has been modified to be able to keep track of the metrics during the intermediary stage. Next, we provide a parallel NN structure and training technique for a single network. This saves a lot of time by eliminating the need to repeatedly retrain the same model. The newly designed algorithm significantly sped up attacks when compared to the previous one. Thus, we propose employing shared layers to overcome memory challenges in parallel structure and improve performance. We evaluated our approaches by presenting non-profiled attacks on ASCAD dataset and a Chip Whisperer-Lite power usage dataset. Power utilisation was studied using both datasets. The shared layers strategy we created was up to 134 times more successful than the prior technique when used to the ASCAD database.